3 research outputs found
A Concise Function Representation for Faster Exact MPE and Constrained Optimisation in Graphical Models
We propose a novel concise function representation for graphical models, a
central theoretical framework that provides the basis for many reasoning tasks.
We then show how we exploit our concise representation based on deterministic
finite state automata within Bucket Elimination (BE), a general approach based
on the concept of variable elimination that can be used to solve many inference
and optimisation tasks, such as most probable explanation and constrained
optimisation. We denote our version of BE as FABE. By using our concise
representation within FABE, we dramatically improve the performance of BE in
terms of runtime and memory requirements. Results achieved by comparing FABE
with state of the art approaches for most probable explanation (i.e., recursive
best-first and structured message passing) and constrained optimisation (i.e.,
CPLEX, GUROBI, and toulbar2) following an established methodology confirm the
efficacy of our concise function representation, showing runtime improvements
of up to 5 orders of magnitude in our tests.Comment: Submitted to IEEE Transactions on Cybernetic